Detection of HEVC Double Compression Based on Deep Representations of In-Loop Filtering and CU Depth Maps

In the field of HEVC (High Efficiency Video Coding) double compression detection, relocated I-frame (RI frame) detection and original GOP size estimation are two significant problems for video forensics. However, little research explores the interconnection between the two problems, and effective methods to resolve them are still lacking. In this paper, a novel feature model called In-loop Filtering and CU Depth Map (IFCDM) is proposed to accurately detect RI frames, and the intrinsic correlation between RI frames and GOP structure is explored, which can be used for original GOP size estimation. Theoretical and statistical analysis of HEVC recompression process is first carried out. Then, sub-features of HEVC in-loop filtering modes and CU partition depth are extracted, and transformed into grey-scale maps to construct IFCDM. A neural network, consisting of tiny Vision Transformer and LSTM, is trained to learn spatial and temporal representations of input features, and further derive the RI frame detection results. Finally, an adaptive periodic analysis algorithm is designed, to integrate the RI frame detection results and estimate the original GOP size of recompressed videos. Experiments show that our method can outperform the existing state-of-the-art methods in both frame level and video level.